Juan-Bosco Lopez-Saez, Jose Antonio Martinez-Rubio, Maria Montes Alvarez, Carmen Gonzalez Carrera, Margarita Dominguez Villar, Antonio Garcia de Lomas Mier, Charo Doménech, Avelino Senra-Varela
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引用次数: 18
摘要
背景:有迹象表明,乳腺癌的死亡率与饮食因素有关,但没有足够大的研究来可靠地描述这种风险是如何受到影响的。为了建立一个逻辑回归方程,从内分泌和代谢方面的因素预测乳腺癌,我们研究了饮食改变的内分泌和代谢风险因素,在西班牙南部患有乳腺癌的女性人群中。患者和方法:我们进行了一项简单的病例对照研究,比较了204名乳腺癌妇女(96名绝经前妇女和108名绝经后妇女)和250名健康对照者。预测变量为基础血糖、胰岛素、糖化血红蛋白(HbA1c)、c肽、胰岛素样生长因子- i (IGF-I)、总胆固醇、甘油三酯、高密度脂蛋白c (HDL-C)、低密度脂蛋白c (LDL-C)、硒和Quetelet指数(BMI)。结果:绝经前和绝经后患者的代谢谱不同,绝经后的代谢改变比绝经前的妇女更大。健康受试者和乳腺癌患者之间的差异明显显著。结论:我们的研究结果在乳腺癌的早期检测,特别是绝经前妇女中具有潜在的实际应用价值;在初级预防方面;以及乳癌发生的数学模型的发展。
Metabolic profile of breast cancer in a population of women in southern Spain.
Background: There are indications that mortality in breast cancer is related with dietary factors, but no study has been large enough to characterise reliably how, this risk is influenced. To establish a logistic regression equation that would predict breast cancer from factors in the endocrinological and metabolic profile, we studied endocrinological and metabolic risk factors that are modified by the diet, in a population of women with breast cancer in southern Spain.
Patients and methods: We carried out a simple a case-control study comparing 204 women with breast cancer (96 premenopausal and 108 postmenopausal women) and 250 healthy control subjects. The predictive variables were basal glycaemia, insulin, glycosylated haemoglobin (HbA1c), C-peptide, insulin-like growth factor-I (IGF-I), total cholesterol, triglycerides, high density lipoprotein-c (HDL-C), low density lipoprotein-c (LDL-C), selenium and Quetelet index (BMI).
Results: The metabolic profile differed between pre- and postmenopausal patients, and metabolic alterations were greater in postmenopausal than in premenopausal women. The differences between healthy subjects and breast cancer patients were clearly significant.
Conclusions: Our findings have several potential practical applications in the early detection of breast cancer, especially in premenopausal women; in primary prevention; and in the development of a mathematical model of breast carcinogenesis.